In this notebook we conduct exploratory factor analyses (EFAs) on the datasets for our studies of concepts of mental life, in which each participants judged the various mental capacities of a particular target entity. We analyze datasets for adults and children from each of our five field sites: the US, Ghana, Thailand, China, and Vanuatu.
This notebook contains a supplemental analysis not presented in the main text, in which we drop participants who gave the same answer on every trial.
country n
US 106
Ghana 88
Thailand 144
China 120
Vanuatu 120
Total 578
the condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be used
funs() is soft deprecated as of dplyr 0.8.0
Please use a list of either functions or lambdas:
# Simple named list:
list(mean = mean, median = median)
# Auto named with `tibble::lst()`:
tibble::lst(mean, median)
# Using lambdas
list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
This warning is displayed once per session.
country n
US 115
Ghana 129
Thailand 151
China 120
Vanuatu 128
Total 643
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.An ultra-Heywood case was detected. Examine the results carefully
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
The estimated weights for the factor scores are probably incorrect. Try a different factor score estimation method.
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 4 and the number of components = 2
Parallel analysis suggests that the number of factors = 3 and the number of components = 2
the condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be used
See All samples, below.
the condition has length > 1 and only the first element will be usedVectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.the condition has length > 1 and only the first element will be usedVectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.the condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be used
Column `country` joining character vector and factor, coercing into character vector
Column `country` joining character vector and factor, coercing into character vector
Column `country` joining character vector and factor, coercing into character vector
Column `country` joining character vector and factor, coercing into character vector
Column `country` joining character vector and factor, coercing into character vector
Column `country` joining character vector and factor, coercing into character vector